Li, M, Meng, W orcid.org/0000-0003-0209-8753, Hu, J et al. (1 more author) (2018) Adaptive Sliding Mode Control of Functional Electrical Stimulation (FES) for Tracking Knee Joint Movement. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID). ISCID 2017, 09-10 Dec 2017, Hangzhou, China. IEEE , pp. 346-349. ISBN 978-1-5386-3675-6
Abstract
Functional electrical stimulation (FES) has shown great potential in helping patients to achieve their joint movements. Since muscle system exists non-linear, time-varying and external disturbances, conventional controllers are difficult to achieve the precise control. In order to improve the accuracy of FES for knee joint movement, in this paper, a RBF neural network based sliding mode control method is designed. An electrical stimulation model of knee joint is first established, the nonlinear performance of RBF neural network is used to approximate the lower limb joint model uncertainties and external disturbances. To determine the width of hidden layer units and the architecture of the neural network, genetic algorithm is introduced to optimize the network structure parameters. Experimental results show that neural network sliding mode control based on genetic algorithm can accurately control the electrical stimulation to obtain the desired joint motion, and can be effectively compensated in the case of external disturbances.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2017, IEEE. This is an author produced version of a paper published in 2017 10th International Symposium on Computational Intelligence and Design. Uploaded in accordance with the publisher's self-archiving policy. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | functional electrical stimulation; knee movement tracking; neural network; sliding mode control |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 01 May 2019 15:08 |
Last Modified: | 01 May 2019 15:08 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ISCID.2017.53 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:145491 |